Using functional information such as pathways, gene sets and other structured lists of biologically functionally relevant information in the interpretation of biological data can be essential for analysis breakthroughs.
Qlucore Omics Explorer (QOE) includes a generic workbench for analysis of functional information gathered in lists, using the Gene Set Enrichment Analysis (GSEA) method. You perform a GSEA analysis simply by pressing the GSEA button, selecting your ranking criteria and pressing "Run". It couldn't be easier. Tight integration with all other functionality together with optimized performance provides extensive benefits over stand alone solutions.
To perform a functional analysis two components are needed: a data set with measurements of the features (variables) and the functional information stored in lists (gene sets). The functional information can typically be gene ontology categories or pathways. Gene set definitions are often acquired from open online repositories such as MSigDB and Reactome.
The Gene Set Enrichment Analysis (GSEA) algorithm is a computational method that determines whether an a priori defined gene set shows strong and concordant associations with a given predictor (for example, differences between two biological groups).
At www.qlucore.com you can view a recorded webinar about GSEA.
References: GSEA: Subramanian, Tamayo, et al. 2005 Proc Natl Acad Sci U S A 102(43):15545-50. MSigDB: Liberzon et al. 2011 Bioinformatics 27(12):1739-4. Reactome: Croft et al. 2014 PMID: 24243840 and Milacic et al. 2012 PMID:24213504 .